Overview

Dataset statistics

Number of variables25
Number of observations3199995
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory610.4 MiB
Average record size in memory200.0 B

Variable types

Numeric22
Categorical3

Alerts

DST_TO_SRC_SECOND_BYTES is highly correlated with IN_BYTES and 9 other fieldsHigh correlation
FIRST_SWITCHED is highly correlated with FLOW_DURATION_MICROSECONDS and 17 other fieldsHigh correlation
FLOW_DURATION_MICROSECONDS is highly correlated with FIRST_SWITCHED and 11 other fieldsHigh correlation
FLOW_DURATION_MILLISECONDS is highly correlated with FIRST_SWITCHED and 11 other fieldsHigh correlation
IN_BYTES is highly correlated with DST_TO_SRC_SECOND_BYTES and 16 other fieldsHigh correlation
IN_PKTS is highly correlated with FIRST_SWITCHED and 13 other fieldsHigh correlation
L4_DST_PORT is highly correlated with DST_TO_SRC_SECOND_BYTES and 5 other fieldsHigh correlation
LAST_SWITCHED is highly correlated with FIRST_SWITCHED and 17 other fieldsHigh correlation
OUT_BYTES is highly correlated with DST_TO_SRC_SECOND_BYTES and 13 other fieldsHigh correlation
OUT_PKTS is highly correlated with DST_TO_SRC_SECOND_BYTES and 14 other fieldsHigh correlation
PROTOCOL is highly correlated with PROTOCOL_MAPHigh correlation
PROTOCOL_MAP is highly correlated with PROTOCOL and 3 other fieldsHigh correlation
SRC_TO_DST_SECOND_BYTES is highly correlated with DST_TO_SRC_SECOND_BYTES and 15 other fieldsHigh correlation
TCP_FLAGS is highly correlated with FIRST_SWITCHED and 17 other fieldsHigh correlation
TCP_WIN_MAX_IN is highly correlated with FIRST_SWITCHED and 10 other fieldsHigh correlation
TCP_WIN_MAX_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 17 other fieldsHigh correlation
TCP_WIN_MIN_IN is highly correlated with FIRST_SWITCHED and 10 other fieldsHigh correlation
TCP_WIN_MIN_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 17 other fieldsHigh correlation
TCP_WIN_MSS_IN is highly correlated with L4_DST_PORTHigh correlation
TCP_WIN_MSS_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 15 other fieldsHigh correlation
TCP_WIN_SCALE_IN is highly correlated with DST_TO_SRC_SECOND_BYTES and 12 other fieldsHigh correlation
TCP_WIN_SCALE_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 14 other fieldsHigh correlation
TOTAL_FLOWS_EXP is highly correlated with FIRST_SWITCHED and 17 other fieldsHigh correlation
FIRST_SWITCHED is highly correlated with FLOW_DURATION_MICROSECONDS and 9 other fieldsHigh correlation
FLOW_DURATION_MICROSECONDS is highly correlated with FIRST_SWITCHED and 3 other fieldsHigh correlation
FLOW_DURATION_MILLISECONDS is highly correlated with FIRST_SWITCHED and 3 other fieldsHigh correlation
IN_BYTES is highly correlated with IN_PKTSHigh correlation
IN_PKTS is highly correlated with IN_BYTES and 1 other fieldsHigh correlation
L4_DST_PORT is highly correlated with FIRST_SWITCHED and 2 other fieldsHigh correlation
LAST_SWITCHED is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
OUT_BYTES is highly correlated with OUT_PKTSHigh correlation
OUT_PKTS is highly correlated with IN_PKTS and 1 other fieldsHigh correlation
PROTOCOL is highly correlated with PROTOCOL_MAP and 1 other fieldsHigh correlation
PROTOCOL_MAP is highly correlated with PROTOCOL and 2 other fieldsHigh correlation
TCP_FLAGS is highly correlated with PROTOCOL and 1 other fieldsHigh correlation
TCP_WIN_MAX_IN is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
TCP_WIN_MAX_OUT is highly correlated with TCP_WIN_MAX_IN and 6 other fieldsHigh correlation
TCP_WIN_MIN_IN is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
TCP_WIN_MIN_OUT is highly correlated with TCP_WIN_MAX_IN and 6 other fieldsHigh correlation
TCP_WIN_MSS_IN is highly correlated with PROTOCOL_MAP and 7 other fieldsHigh correlation
TCP_WIN_MSS_OUT is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
TCP_WIN_SCALE_IN is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
TCP_WIN_SCALE_OUT is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
TOTAL_FLOWS_EXP is highly correlated with FIRST_SWITCHED and 9 other fieldsHigh correlation
DST_TO_SRC_SECOND_BYTES is highly correlated with OUT_BYTES and 5 other fieldsHigh correlation
FIRST_SWITCHED is highly correlated with LAST_SWITCHED and 6 other fieldsHigh correlation
FLOW_DURATION_MICROSECONDS is highly correlated with FLOW_DURATION_MILLISECONDS and 4 other fieldsHigh correlation
FLOW_DURATION_MILLISECONDS is highly correlated with FLOW_DURATION_MICROSECONDS and 4 other fieldsHigh correlation
IN_BYTES is highly correlated with FLOW_DURATION_MICROSECONDS and 7 other fieldsHigh correlation
IN_PKTS is highly correlated with FLOW_DURATION_MICROSECONDS and 6 other fieldsHigh correlation
L4_DST_PORT is highly correlated with TCP_WIN_MSS_INHigh correlation
LAST_SWITCHED is highly correlated with FIRST_SWITCHED and 6 other fieldsHigh correlation
OUT_BYTES is highly correlated with DST_TO_SRC_SECOND_BYTES and 7 other fieldsHigh correlation
OUT_PKTS is highly correlated with FLOW_DURATION_MICROSECONDS and 7 other fieldsHigh correlation
PROTOCOL is highly correlated with PROTOCOL_MAPHigh correlation
PROTOCOL_MAP is highly correlated with PROTOCOL and 3 other fieldsHigh correlation
SRC_TO_DST_SECOND_BYTES is highly correlated with DST_TO_SRC_SECOND_BYTES and 6 other fieldsHigh correlation
TCP_FLAGS is highly correlated with OUT_PKTS and 8 other fieldsHigh correlation
TCP_WIN_MAX_IN is highly correlated with PROTOCOL_MAP and 7 other fieldsHigh correlation
TCP_WIN_MAX_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 15 other fieldsHigh correlation
TCP_WIN_MIN_IN is highly correlated with PROTOCOL_MAP and 7 other fieldsHigh correlation
TCP_WIN_MIN_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 15 other fieldsHigh correlation
TCP_WIN_MSS_IN is highly correlated with L4_DST_PORTHigh correlation
TCP_WIN_MSS_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 11 other fieldsHigh correlation
TCP_WIN_SCALE_IN is highly correlated with FIRST_SWITCHED and 10 other fieldsHigh correlation
TCP_WIN_SCALE_OUT is highly correlated with DST_TO_SRC_SECOND_BYTES and 11 other fieldsHigh correlation
TOTAL_FLOWS_EXP is highly correlated with FIRST_SWITCHED and 6 other fieldsHigh correlation
LABEL is highly correlated with PROTOCOL_MAPHigh correlation
PROTOCOL is highly correlated with PROTOCOL_MAPHigh correlation
PROTOCOL_MAP is highly correlated with LABEL and 1 other fieldsHigh correlation
DST_TO_SRC_SECOND_BYTES is highly correlated with OUT_BYTES and 1 other fieldsHigh correlation
FIRST_SWITCHED is highly correlated with FLOW_DURATION_MICROSECONDS and 13 other fieldsHigh correlation
FLOW_DURATION_MICROSECONDS is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
FLOW_DURATION_MILLISECONDS is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
IN_BYTES is highly correlated with IN_PKTS and 1 other fieldsHigh correlation
IN_PKTS is highly correlated with IN_BYTES and 3 other fieldsHigh correlation
L4_DST_PORT is highly correlated with FIRST_SWITCHED and 6 other fieldsHigh correlation
L4_SRC_PORT is highly correlated with FIRST_SWITCHED and 5 other fieldsHigh correlation
LAST_SWITCHED is highly correlated with FIRST_SWITCHED and 13 other fieldsHigh correlation
OUT_BYTES is highly correlated with DST_TO_SRC_SECOND_BYTES and 2 other fieldsHigh correlation
OUT_PKTS is highly correlated with DST_TO_SRC_SECOND_BYTES and 3 other fieldsHigh correlation
PROTOCOL is highly correlated with L4_SRC_PORT and 3 other fieldsHigh correlation
PROTOCOL_MAP is highly correlated with PROTOCOL and 2 other fieldsHigh correlation
SRC_TO_DST_SECOND_BYTES is highly correlated with IN_PKTSHigh correlation
TCP_FLAGS is highly correlated with FIRST_SWITCHED and 11 other fieldsHigh correlation
TCP_WIN_MAX_IN is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
TCP_WIN_MAX_OUT is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
TCP_WIN_MIN_IN is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
TCP_WIN_MIN_OUT is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
TCP_WIN_MSS_IN is highly correlated with PROTOCOL and 8 other fieldsHigh correlation
TCP_WIN_SCALE_IN is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
TCP_WIN_SCALE_OUT is highly correlated with FIRST_SWITCHED and 12 other fieldsHigh correlation
TOTAL_FLOWS_EXP is highly correlated with FIRST_SWITCHED and 13 other fieldsHigh correlation
LABEL is highly correlated with FIRST_SWITCHED and 16 other fieldsHigh correlation
DST_TO_SRC_SECOND_BYTES is highly skewed (γ1 = 805.7644402) Skewed
IN_BYTES is highly skewed (γ1 = 144.2181232) Skewed
IN_PKTS is highly skewed (γ1 = 110.4673302) Skewed
OUT_BYTES is highly skewed (γ1 = 483.1987086) Skewed
OUT_PKTS is highly skewed (γ1 = 353.9178486) Skewed
SRC_TO_DST_SECOND_BYTES is highly skewed (γ1 = 196.7848346) Skewed
LABEL is uniformly distributed Uniform
TOTAL_FLOWS_EXP has unique values Unique
DST_TO_SRC_SECOND_BYTES has 322775 (10.1%) zeros Zeros
FLOW_DURATION_MICROSECONDS has 278869 (8.7%) zeros Zeros
FLOW_DURATION_MILLISECONDS has 979266 (30.6%) zeros Zeros
L4_DST_PORT has 70826 (2.2%) zeros Zeros
L4_SRC_PORT has 70805 (2.2%) zeros Zeros
OUT_BYTES has 322775 (10.1%) zeros Zeros
OUT_PKTS has 322775 (10.1%) zeros Zeros
TCP_FLAGS has 450350 (14.1%) zeros Zeros
TCP_WIN_MAX_IN has 453954 (14.2%) zeros Zeros
TCP_WIN_MAX_OUT has 1370736 (42.8%) zeros Zeros
TCP_WIN_MIN_IN has 453954 (14.2%) zeros Zeros
TCP_WIN_MIN_OUT has 1370736 (42.8%) zeros Zeros
TCP_WIN_MSS_IN has 1098426 (34.3%) zeros Zeros
TCP_WIN_MSS_OUT has 1962244 (61.3%) zeros Zeros
TCP_WIN_SCALE_IN has 1908794 (59.6%) zeros Zeros
TCP_WIN_SCALE_OUT has 1966043 (61.4%) zeros Zeros

Reproduction

Analysis started2022-02-27 21:23:18.681541
Analysis finished2022-02-27 21:33:32.750040
Duration10 minutes and 14.07 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

DST_TO_SRC_SECOND_BYTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct13368
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean719.1777078
Minimum0
Maximum44138464
Zeros322775
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:32.889964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median52
Q3112
95-th percentile330
Maximum44138464
Range44138464
Interquartile range (IQR)72

Descriptive statistics

Standard deviation32794.3607
Coefficient of variation (CV)45.59980147
Kurtosis1032563.386
Mean719.1777078
Median Absolute Deviation (MAD)52
Skewness805.7644402
Sum2301365069
Variance1075470094
MonotonicityNot monotonic
2022-02-27T15:33:33.068556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40759312
23.7%
112676342
21.1%
52516446
16.1%
0322775
10.1%
60235601
 
7.4%
164188430
 
5.9%
10422213
 
0.7%
15210808
 
0.3%
7610549
 
0.3%
20878653
 
0.3%
Other values (13358)448866
14.0%
ValueCountFrequency (%)
0322775
10.1%
32385
 
< 0.1%
356
 
< 0.1%
397
 
< 0.1%
40759312
23.7%
4116
 
< 0.1%
44819
 
< 0.1%
476
 
< 0.1%
48739
 
< 0.1%
4938
 
< 0.1%
ValueCountFrequency (%)
441384641
< 0.1%
91829031
< 0.1%
85616931
< 0.1%
68415921
< 0.1%
67687861
< 0.1%
50811831
< 0.1%
49862671
< 0.1%
48759441
< 0.1%
48259641
< 0.1%
47893001
< 0.1%

FIRST_SWITCHED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32299
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1617637850
Minimum1616660040
Maximum1618246851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:33.236203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1616660040
5-th percentile1616660707
Q11616663520
median1618223404
Q31618236901
95-th percentile1618244556
Maximum1618246851
Range1586811
Interquartile range (IQR)1573381

Descriptive statistics

Standard deviation755560.8136
Coefficient of variation (CV)0.0004670766164
Kurtosis-1.732801416
Mean1617637850
Median Absolute Deviation (MAD)23446
Skewness-0.5147522981
Sum5.176433032 × 1015
Variance5.70872143 × 1011
MonotonicityNot monotonic
2022-02-27T15:33:33.423195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161666009511393
 
0.4%
161666181310722
 
0.3%
16166653576457
 
0.2%
16182238404764
 
0.1%
16182237204665
 
0.1%
16166653583184
 
0.1%
16182236852512
 
0.1%
16182261872465
 
0.1%
16182255872462
 
0.1%
16182260672369
 
0.1%
Other values (32289)3149002
98.4%
ValueCountFrequency (%)
161666004037
< 0.1%
161666004167
< 0.1%
161666004249
< 0.1%
161666004349
< 0.1%
161666004471
< 0.1%
161666004567
< 0.1%
161666004662
< 0.1%
161666004749
< 0.1%
161666004852
< 0.1%
161666004953
< 0.1%
ValueCountFrequency (%)
16182468511
 
< 0.1%
16182468503
< 0.1%
16182468492
< 0.1%
16182468482
< 0.1%
16182468473
< 0.1%
16182468461
 
< 0.1%
16182468453
< 0.1%
16182468441
 
< 0.1%
16182468433
< 0.1%
16182468423
< 0.1%

FLOW_DURATION_MICROSECONDS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1342276
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35610998.83
Minimum0
Maximum119999510
Zeros278869
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:33.612666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1800
median766712
Q360283192
95-th percentile112239595.5
Maximum119999510
Range119999510
Interquartile range (IQR)60282392

Descriptive statistics

Standard deviation42934683.85
Coefficient of variation (CV)1.20565795
Kurtosis-0.9794069857
Mean35610998.83
Median Absolute Deviation (MAD)766712
Skewness0.7227344148
Sum1.139550182 × 1014
Variance1.843387077 × 1015
MonotonicityNot monotonic
2022-02-27T15:33:33.811364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0278869
 
8.7%
2821473
 
< 0.1%
2771434
 
< 0.1%
2761414
 
< 0.1%
2891407
 
< 0.1%
2781404
 
< 0.1%
2851403
 
< 0.1%
2751393
 
< 0.1%
2741392
 
< 0.1%
2911384
 
< 0.1%
Other values (1342266)2908422
90.9%
ValueCountFrequency (%)
0278869
8.7%
1166
 
< 0.1%
2283
 
< 0.1%
3318
 
< 0.1%
4292
 
< 0.1%
5749
 
< 0.1%
6331
 
< 0.1%
7251
 
< 0.1%
8187
 
< 0.1%
9127
 
< 0.1%
ValueCountFrequency (%)
1199995101
< 0.1%
1199990401
< 0.1%
1199983251
< 0.1%
1199983241
< 0.1%
1199983201
< 0.1%
1199983151
< 0.1%
1199982911
< 0.1%
1199982381
< 0.1%
1199981711
< 0.1%
1199980671
< 0.1%

FLOW_DURATION_MILLISECONDS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct83598
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35610.542
Minimum0
Maximum119999
Zeros979266
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:33.990890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median766
Q360283
95-th percentile112239
Maximum119999
Range119999
Interquartile range (IQR)60283

Descriptive statistics

Standard deviation42934.64825
Coefficient of variation (CV)1.205672417
Kurtosis-0.9794054719
Mean35610.542
Median Absolute Deviation (MAD)766
Skewness0.7227352666
Sum1.139535563 × 1011
Variance1843384021
MonotonicityNot monotonic
2022-02-27T15:33:34.181309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0979266
30.6%
1144308
 
4.5%
1625144
 
0.8%
1521586
 
0.7%
1415073
 
0.5%
2814175
 
0.4%
214020
 
0.4%
2911079
 
0.3%
277935
 
0.2%
177619
 
0.2%
Other values (83588)1959790
61.2%
ValueCountFrequency (%)
0979266
30.6%
1144308
 
4.5%
214020
 
0.4%
36059
 
0.2%
43502
 
0.1%
52527
 
0.1%
61586
 
< 0.1%
71002
 
< 0.1%
8839
 
< 0.1%
9712
 
< 0.1%
ValueCountFrequency (%)
1199992
 
< 0.1%
11999811
< 0.1%
11999714
< 0.1%
1199962
 
< 0.1%
11999524
< 0.1%
1199947
 
< 0.1%
1199931
 
< 0.1%
1199924
 
< 0.1%
1199907
 
< 0.1%
1199894
 
< 0.1%

IN_BYTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct29391
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5010.958731
Minimum28
Maximum109706171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:34.369296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile44
Q144
median413
Q31065
95-th percentile1621
Maximum109706171
Range109706143
Interquartile range (IQR)1021

Descriptive statistics

Standard deviation343852.7671
Coefficient of variation (CV)68.62015545
Kurtosis25521.12443
Mean5010.958731
Median Absolute Deviation (MAD)369
Skewness144.2181232
Sum1.603504288 × 1010
Variance1.182347254 × 1011
MonotonicityNot monotonic
2022-02-27T15:33:34.547881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44805927
 
25.2%
216136006
 
4.3%
5238139
 
1.2%
8436749
 
1.1%
7229763
 
0.9%
18927453
 
0.9%
4026613
 
0.8%
8021515
 
0.7%
7316126
 
0.5%
4114649
 
0.5%
Other values (29381)2047055
64.0%
ValueCountFrequency (%)
28130
 
< 0.1%
2951
 
< 0.1%
30158
 
< 0.1%
3122
 
< 0.1%
321210
 
< 0.1%
3313
 
< 0.1%
34287
 
< 0.1%
3521
 
< 0.1%
366615
0.2%
3752
 
< 0.1%
ValueCountFrequency (%)
1097061711
< 0.1%
911979741
< 0.1%
897464151
< 0.1%
868065811
< 0.1%
794928981
< 0.1%
777563231
< 0.1%
763563121
< 0.1%
722626861
< 0.1%
722248181
< 0.1%
702314671
< 0.1%

IN_PKTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct3128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.83783131
Minimum1
Maximum81219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:34.729915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q311
95-th percentile16
Maximum81219
Range81218
Interquartile range (IQR)10

Descriptive statistics

Standard deviation288.0942646
Coefficient of variation (CV)20.81932191
Kurtosis16407.43902
Mean13.83783131
Median Absolute Deviation (MAD)3
Skewness110.4673302
Sum44280991
Variance82998.30531
MonotonicityNot monotonic
2022-02-27T15:33:34.912935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11241036
38.8%
11691934
21.6%
12227468
 
7.1%
4165141
 
5.2%
2136518
 
4.3%
15115536
 
3.6%
13104691
 
3.3%
1493920
 
2.9%
358464
 
1.8%
1657510
 
1.8%
Other values (3118)307777
 
9.6%
ValueCountFrequency (%)
11241036
38.8%
2136518
 
4.3%
358464
 
1.8%
4165141
 
5.2%
525762
 
0.8%
629962
 
0.9%
748376
 
1.5%
828380
 
0.9%
925741
 
0.8%
1031360
 
1.0%
ValueCountFrequency (%)
812191
< 0.1%
745101
< 0.1%
621701
< 0.1%
613801
< 0.1%
609051
< 0.1%
554181
< 0.1%
544921
< 0.1%
542101
< 0.1%
540501
< 0.1%
530341
< 0.1%

L4_DST_PORT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65536
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10776.89484
Minimum0
Maximum65535
Zeros70826
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:35.098439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53
Q180
median80
Q313085
95-th percentile56612
Maximum65535
Range65535
Interquartile range (IQR)13005

Descriptive statistics

Standard deviation18968.62324
Coefficient of variation (CV)1.760119545
Kurtosis1.009762313
Mean10776.89484
Median Absolute Deviation (MAD)0
Skewness1.583077138
Sum3.44860096 × 1010
Variance359808667.6
MonotonicityNot monotonic
2022-02-27T15:33:35.267081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801608279
50.3%
53274254
 
8.6%
443152150
 
4.8%
070826
 
2.2%
4792134372
 
1.1%
5778233911
 
1.1%
2232251
 
1.0%
99939737
 
0.3%
59386121
 
0.2%
64436053
 
0.2%
Other values (65526)972041
30.4%
ValueCountFrequency (%)
070826
2.2%
12700
 
0.1%
225
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
512
 
< 0.1%
612
 
< 0.1%
718
 
< 0.1%
812
 
< 0.1%
913
 
< 0.1%
ValueCountFrequency (%)
65535106
< 0.1%
6553414
 
< 0.1%
6553313
 
< 0.1%
6553214
 
< 0.1%
6553113
 
< 0.1%
6553012
 
< 0.1%
6552912
 
< 0.1%
6552812
 
< 0.1%
6552712
 
< 0.1%
6552612
 
< 0.1%

L4_SRC_PORT
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct64607
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41909.27575
Minimum0
Maximum65535
Zeros70805
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:35.442166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4147
Q136084
median48958
Q350114
95-th percentile60162
Maximum65535
Range65535
Interquartile range (IQR)14030

Descriptive statistics

Standard deviation15835.10346
Coefficient of variation (CV)0.3778424508
Kurtosis0.7967380633
Mean41909.27575
Median Absolute Deviation (MAD)6433
Skewness-1.253191379
Sum1.341094728 × 1011
Variance250750501.6
MonotonicityNot monotonic
2022-02-27T15:33:35.806723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49214458859
 
14.3%
48958262213
 
8.2%
070805
 
2.2%
4921565997
 
2.1%
44339768
 
1.2%
4792124939
 
0.8%
4947011551
 
0.4%
99934676
 
0.1%
543904631
 
0.1%
237674161
 
0.1%
Other values (64597)2252395
70.4%
ValueCountFrequency (%)
070805
2.2%
41
 
< 0.1%
212
 
< 0.1%
221088
 
< 0.1%
391
 
< 0.1%
411
 
< 0.1%
5112
 
< 0.1%
5339
 
< 0.1%
731
 
< 0.1%
802299
 
0.1%
ValueCountFrequency (%)
6553519
 
< 0.1%
6553423
 
< 0.1%
6553316
 
< 0.1%
65532296
< 0.1%
6553113
 
< 0.1%
6553026
 
< 0.1%
6552916
 
< 0.1%
6552821
 
< 0.1%
6552712
 
< 0.1%
6552614
 
< 0.1%

LAST_SWITCHED
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31882
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1617637886
Minimum1616660040
Maximum1618246852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:35.984725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1616660040
5-th percentile1616660708
Q11616663521
median1618223409
Q31618236902
95-th percentile1618244614
Maximum1618246852
Range1586812
Interquartile range (IQR)1573380.5

Descriptive statistics

Standard deviation755587.6554
Coefficient of variation (CV)0.0004670931993
Kurtosis-1.732800619
Mean1617637886
Median Absolute Deviation (MAD)23443
Skewness-0.5147494189
Sum5.176433146 × 1015
Variance5.70912705 × 1011
MonotonicityNot monotonic
2022-02-27T15:33:36.172708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161822482416360
 
0.5%
161822629912739
 
0.4%
161666009511393
 
0.4%
161666181310734
 
0.3%
16182262989297
 
0.3%
16166653576454
 
0.2%
16182236905505
 
0.2%
16182238304765
 
0.1%
16182250654694
 
0.1%
16182240054323
 
0.1%
Other values (31872)3113731
97.3%
ValueCountFrequency (%)
161666004028
< 0.1%
161666004149
< 0.1%
161666004250
< 0.1%
161666004337
< 0.1%
161666004446
< 0.1%
161666004560
< 0.1%
161666004667
< 0.1%
161666004748
< 0.1%
161666004844
< 0.1%
161666004946
< 0.1%
ValueCountFrequency (%)
16182468528
 
< 0.1%
161824685164
< 0.1%
161824685062
< 0.1%
161824684960
< 0.1%
161824684864
< 0.1%
161824684764
< 0.1%
161824684676
< 0.1%
161824684566
< 0.1%
161824684465
< 0.1%
161824684377
< 0.1%

OUT_BYTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct28631
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11254.86027
Minimum0
Maximum529407671
Zeros322775
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:36.363172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q140
median272
Q3528
95-th percentile833
Maximum529407671
Range529407671
Interquartile range (IQR)488

Descriptive statistics

Standard deviation486863.4009
Coefficient of variation (CV)43.25805823
Kurtosis459480.0315
Mean11254.86027
Median Absolute Deviation (MAD)256
Skewness483.1987086
Sum3.601549659 × 1010
Variance2.370359711 × 1011
MonotonicityNot monotonic
2022-02-27T15:33:36.549172image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40755994
23.6%
528687897
21.5%
624329413
10.3%
0322775
10.1%
164142226
 
4.4%
736102019
 
3.2%
67656495
 
1.8%
58044164
 
1.4%
10426225
 
0.8%
208722751
 
0.7%
Other values (28621)710036
22.2%
ValueCountFrequency (%)
0322775
10.1%
3270
 
< 0.1%
397
 
< 0.1%
40755994
23.6%
414
 
< 0.1%
44306
 
< 0.1%
476
 
< 0.1%
4820
 
< 0.1%
514
 
< 0.1%
5219335
 
0.6%
ValueCountFrequency (%)
5294076711
< 0.1%
2293965621
< 0.1%
1339085761
< 0.1%
1050066701
< 0.1%
1030945181
< 0.1%
1013876751
< 0.1%
939359621
< 0.1%
868584251
< 0.1%
815728791
< 0.1%
763473191
< 0.1%

OUT_PKTS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3519
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.86836448
Minimum0
Maximum384251
Zeros322775
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:36.733187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q310
95-th percentile14
Maximum384251
Range384251
Interquartile range (IQR)9

Descriptive statistics

Standard deviation394.5684134
Coefficient of variation (CV)24.86509645
Kurtosis293452.371
Mean15.86836448
Median Absolute Deviation (MAD)3
Skewness353.9178486
Sum50778687
Variance155684.2328
MonotonicityNot monotonic
2022-02-27T15:33:36.919683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11012725
31.6%
10718511
22.5%
12340261
 
10.6%
0322775
 
10.1%
3166500
 
5.2%
14124112
 
3.9%
2107617
 
3.4%
1372244
 
2.3%
1164165
 
2.0%
647465
 
1.5%
Other values (3509)223620
 
7.0%
ValueCountFrequency (%)
0322775
 
10.1%
11012725
31.6%
2107617
 
3.4%
3166500
 
5.2%
437497
 
1.2%
529087
 
0.9%
647465
 
1.5%
725056
 
0.8%
829200
 
0.9%
932175
 
1.0%
ValueCountFrequency (%)
3842511
< 0.1%
1529371
< 0.1%
992591
< 0.1%
724631
< 0.1%
721191
< 0.1%
677501
< 0.1%
639691
< 0.1%
586401
< 0.1%
547891
< 0.1%
533171
< 0.1%

PROTOCOL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.4 MiB
6
2749645 
17
379545 
1
 
70804
47
 
1

Length

Max length2
Median length1
Mean length1.11860831
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row6
2nd row1
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
62749645
85.9%
17379545
 
11.9%
170804
 
2.2%
471
 
< 0.1%

Length

2022-02-27T15:33:37.087574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T15:33:37.176797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
62749645
85.9%
17379545
 
11.9%
170804
 
2.2%
471
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PROTOCOL_MAP
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.4 MiB
0
2749645 
1
450350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02749645
85.9%
1450350
 
14.1%

Length

2022-02-27T15:33:37.277771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T15:33:37.360106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
02749645
85.9%
1450350
 
14.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SRC_TO_DST_SECOND_BYTES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct10500
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.6178128
Minimum28
Maximum6238685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:37.468730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile44
Q144
median97
Q3326
95-th percentile569
Maximum6238685
Range6238657
Interquartile range (IQR)282

Descriptive statistics

Standard deviation11069.90799
Coefficient of variation (CV)28.93202464
Kurtosis70131.61631
Mean382.6178128
Median Absolute Deviation (MAD)53
Skewness196.7848346
Sum1224375088
Variance122542862.9
MonotonicityNot monotonic
2022-02-27T15:33:37.642826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44805996
25.2%
112232308
 
7.3%
407144520
 
4.5%
347121071
 
3.8%
216111546
 
3.5%
45195465
 
3.0%
32680804
 
2.5%
8455305
 
1.7%
5249992
 
1.6%
49544213
 
1.4%
Other values (10490)1458775
45.6%
ValueCountFrequency (%)
28372
 
< 0.1%
2951
 
< 0.1%
30158
 
< 0.1%
3122
 
< 0.1%
321210
 
< 0.1%
3313
 
< 0.1%
34339
 
< 0.1%
3521
 
< 0.1%
366615
0.2%
3752
 
< 0.1%
ValueCountFrequency (%)
62386851
< 0.1%
51887571
< 0.1%
47210281
< 0.1%
37790941
< 0.1%
25155471
< 0.1%
25099011
< 0.1%
23770631
< 0.1%
22820731
< 0.1%
20549901
< 0.1%
20377311
< 0.1%

TCP_FLAGS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.86292447
Minimum0
Maximum247
Zeros450350
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:37.809026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median24
Q327
95-th percentile27
Maximum247
Range247
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.02711753
Coefficient of variation (CV)0.5048157711
Kurtosis22.1641373
Mean19.86292447
Median Absolute Deviation (MAD)3
Skewness-0.09444925485
Sum63561259
Variance100.543086
MonotonicityNot monotonic
2022-02-27T15:33:37.979609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
27893355
27.9%
22736729
23.0%
0450350
14.1%
24436017
13.6%
2163747
 
5.1%
26159675
 
5.0%
19144608
 
4.5%
25120646
 
3.8%
3134800
 
1.1%
1620210
 
0.6%
Other values (29)39858
 
1.2%
ValueCountFrequency (%)
0450350
14.1%
2163747
 
5.1%
4793
 
< 0.1%
81
 
< 0.1%
1620210
 
0.6%
174236
 
0.1%
181471
 
< 0.1%
19144608
 
4.5%
203333
 
0.1%
21466
 
< 0.1%
ValueCountFrequency (%)
2471
 
< 0.1%
22352
 
< 0.1%
2229
 
< 0.1%
219142
< 0.1%
218200
< 0.1%
21444
 
< 0.1%
2112
 
< 0.1%
2103
 
< 0.1%
1971
 
< 0.1%
19413
 
< 0.1%

TCP_WIN_MAX_IN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6007
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26167.26979
Minimum0
Maximum65535
Zeros453954
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:38.148143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1507
median1024
Q364860
95-th percentile64860
Maximum65535
Range65535
Interquartile range (IQR)64353

Descriptive statistics

Standard deviation31171.86482
Coefficient of variation (CV)1.191254
Kurtosis-1.807227154
Mean26167.26979
Median Absolute Deviation (MAD)1024
Skewness0.4222695377
Sum8.37351325 × 1010
Variance971685156.3
MonotonicityNot monotonic
2022-02-27T15:33:38.324696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
648601113407
34.8%
1024818894
25.6%
507488330
15.3%
0453954
14.2%
64240102761
 
3.2%
2920025531
 
0.8%
6533023310
 
0.7%
11816541
 
0.5%
120015067
 
0.5%
50111571
 
0.4%
Other values (5997)130629
 
4.1%
ValueCountFrequency (%)
0453954
14.2%
122
 
< 0.1%
216
 
< 0.1%
323
 
< 0.1%
429
 
< 0.1%
552
 
< 0.1%
66
 
< 0.1%
76
 
< 0.1%
1127
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
6553510844
0.3%
655301
 
< 0.1%
6552011
 
< 0.1%
6551815
 
< 0.1%
655142
 
< 0.1%
655061
 
< 0.1%
655022
 
< 0.1%
654913
 
< 0.1%
6547225
 
< 0.1%
6545325
 
< 0.1%

TCP_WIN_MAX_OUT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5164
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11590.09776
Minimum0
Maximum65535
Zeros1370736
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:38.577330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median114
Q327960
95-th percentile27960
Maximum65535
Range65535
Interquartile range (IQR)27960

Descriptive statistics

Standard deviation15268.28393
Coefficient of variation (CV)1.317355923
Kurtosis0.5050572117
Mean11590.09776
Median Absolute Deviation (MAD)114
Skewness0.9970974134
Sum3.708825488 × 1010
Variance233120494.2
MonotonicityNot monotonic
2022-02-27T15:33:38.757135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01370736
42.8%
279601157626
36.2%
114487550
 
15.2%
6553539143
 
1.2%
6424010609
 
0.3%
292007907
 
0.2%
20536357
 
0.2%
5025230
 
0.2%
5014456
 
0.1%
289604426
 
0.1%
Other values (5154)105955
 
3.3%
ValueCountFrequency (%)
01370736
42.8%
11
 
< 0.1%
714
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
10404
 
< 0.1%
11157
 
< 0.1%
1220
 
< 0.1%
134
 
< 0.1%
143
 
< 0.1%
ValueCountFrequency (%)
6553539143
1.2%
655342
 
< 0.1%
655202
 
< 0.1%
654761
 
< 0.1%
654724
 
< 0.1%
654343
 
< 0.1%
6542212
 
< 0.1%
654144
 
< 0.1%
65392219
 
< 0.1%
653871
 
< 0.1%

TCP_WIN_MIN_IN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6119
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26131.65557
Minimum0
Maximum65535
Zeros453954
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:38.938673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1507
median1024
Q364860
95-th percentile64860
Maximum65535
Range65535
Interquartile range (IQR)64353

Descriptive statistics

Standard deviation31172.18669
Coefficient of variation (CV)1.19288985
Kurtosis-1.805879303
Mean26131.65557
Median Absolute Deviation (MAD)1024
Skewness0.4245852392
Sum8.362116715 × 1010
Variance971705223.1
MonotonicityNot monotonic
2022-02-27T15:33:39.114886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
648601113373
34.8%
1024834129
26.1%
507488523
15.3%
0453954
14.2%
64240102083
 
3.2%
2920025578
 
0.8%
6533023310
 
0.7%
11816541
 
0.5%
819215177
 
0.5%
50112265
 
0.4%
Other values (6109)115062
 
3.6%
ValueCountFrequency (%)
0453954
14.2%
124
 
< 0.1%
216
 
< 0.1%
323
 
< 0.1%
428
 
< 0.1%
551
 
< 0.1%
67
 
< 0.1%
75
 
< 0.1%
1127
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
6553510807
0.3%
655301
 
< 0.1%
655201
 
< 0.1%
6551815
 
< 0.1%
655061
 
< 0.1%
655022
 
< 0.1%
654913
 
< 0.1%
6547216
 
< 0.1%
6545310
 
< 0.1%
6545050
 
< 0.1%

TCP_WIN_MIN_OUT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4947
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11577.29283
Minimum0
Maximum65535
Zeros1370736
Zeros (%)42.8%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:39.294066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median114
Q327960
95-th percentile27960
Maximum65535
Range65535
Interquartile range (IQR)27960

Descriptive statistics

Standard deviation15259.8445
Coefficient of variation (CV)1.318084005
Kurtosis0.5065499407
Mean11577.29283
Median Absolute Deviation (MAD)114
Skewness0.9977787948
Sum3.704727918 × 1010
Variance232862854.1
MonotonicityNot monotonic
2022-02-27T15:33:39.475062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01370736
42.8%
279601156991
36.2%
114488169
 
15.3%
6553539069
 
1.2%
6424010546
 
0.3%
292008708
 
0.3%
20536109
 
0.2%
5025419
 
0.2%
5014501
 
0.1%
289604426
 
0.1%
Other values (4937)105321
 
3.3%
ValueCountFrequency (%)
01370736
42.8%
11
 
< 0.1%
714
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
10408
 
< 0.1%
11153
 
< 0.1%
1221
 
< 0.1%
133
 
< 0.1%
143
 
< 0.1%
ValueCountFrequency (%)
6553539069
1.2%
655342
 
< 0.1%
655202
 
< 0.1%
654761
 
< 0.1%
654721
 
< 0.1%
654343
 
< 0.1%
654144
 
< 0.1%
654091
 
< 0.1%
65392211
 
< 0.1%
653881
 
< 0.1%

TCP_WIN_MSS_IN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean940.7742265
Minimum0
Maximum8960
Zeros1098426
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:39.660566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1410
Q31460
95-th percentile1460
Maximum8960
Range8960
Interquartile range (IQR)1460

Descriptive statistics

Standard deviation683.0237723
Coefficient of variation (CV)0.726023049
Kurtosis-0.4639501855
Mean940.7742265
Median Absolute Deviation (MAD)50
Skewness-0.5551356417
Sum3010472821
Variance466521.4736
MonotonicityNot monotonic
2022-02-27T15:33:39.832677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14101113529
34.8%
01098426
34.3%
1460933660
29.2%
139023319
 
0.7%
14246003
 
0.2%
14125237
 
0.2%
14524777
 
0.1%
14404532
 
0.1%
14002801
 
0.1%
14202374
 
0.1%
Other values (71)5337
 
0.2%
ValueCountFrequency (%)
01098426
34.3%
13
 
< 0.1%
26550
 
< 0.1%
5121
 
< 0.1%
536492
 
< 0.1%
6407
 
< 0.1%
10245
 
< 0.1%
11038
 
< 0.1%
1104159
 
< 0.1%
1146148
 
< 0.1%
ValueCountFrequency (%)
8960188
 
< 0.1%
88601
 
< 0.1%
19382
 
< 0.1%
151062
 
< 0.1%
14801
 
< 0.1%
1460933660
29.2%
145659
 
< 0.1%
14524777
 
0.1%
145032
 
< 0.1%
144845
 
< 0.1%

TCP_WIN_MSS_OUT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.031341
Minimum0
Maximum65475
Zeros1962244
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:39.997349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31410
95-th percentile1410
Maximum65475
Range65475
Interquartile range (IQR)1410

Descriptive statistics

Standard deviation694.7102889
Coefficient of variation (CV)1.269964327
Kurtosis47.93809941
Mean547.031341
Median Absolute Deviation (MAD)0
Skewness1.131881283
Sum1750497556
Variance482622.3855
MonotonicityNot monotonic
2022-02-27T15:33:40.162516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
01962244
61.3%
14101162166
36.3%
146036889
 
1.2%
144026358
 
0.8%
14304778
 
0.1%
14003176
 
0.1%
14201637
 
0.1%
1380535
 
< 0.1%
1450534
 
< 0.1%
1392432
 
< 0.1%
Other values (29)1246
 
< 0.1%
ValueCountFrequency (%)
01962244
61.3%
12201
 
< 0.1%
12402
 
< 0.1%
130067
 
< 0.1%
13306
 
< 0.1%
134824
 
< 0.1%
13505
 
< 0.1%
1360205
 
< 0.1%
13724
 
< 0.1%
1380535
 
< 0.1%
ValueCountFrequency (%)
654752
 
< 0.1%
896142
 
< 0.1%
896059
 
< 0.1%
8860219
 
< 0.1%
443010
 
< 0.1%
1480141
 
< 0.1%
14642
 
< 0.1%
146036889
1.2%
145617
 
< 0.1%
1452234
 
< 0.1%

TCP_WIN_SCALE_IN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.83365224
Minimum0
Maximum14
Zeros1908794
Zeros (%)59.6%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:40.302884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile7
Maximum14
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.458264686
Coefficient of variation (CV)1.220426641
Kurtosis-1.819708373
Mean2.83365224
Median Absolute Deviation (MAD)0
Skewness0.4089416731
Sum9067673
Variance11.95959464
MonotonicityNot monotonic
2022-02-27T15:33:40.432836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
01908794
59.6%
71209175
37.8%
870683
 
2.2%
27902
 
0.2%
61936
 
0.1%
9449
 
< 0.1%
4235
 
< 0.1%
5232
 
< 0.1%
10226
 
< 0.1%
1138
 
< 0.1%
Other values (4)225
 
< 0.1%
ValueCountFrequency (%)
01908794
59.6%
1138
 
< 0.1%
27902
 
0.2%
371
 
< 0.1%
4235
 
< 0.1%
5232
 
< 0.1%
61936
 
0.1%
71209175
37.8%
870683
 
2.2%
9449
 
< 0.1%
ValueCountFrequency (%)
143
 
< 0.1%
12109
 
< 0.1%
1142
 
< 0.1%
10226
 
< 0.1%
9449
 
< 0.1%
870683
 
2.2%
71209175
37.8%
61936
 
0.1%
5232
 
< 0.1%
4235
 
< 0.1%

TCP_WIN_SCALE_OUT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.072126675
Minimum0
Maximum14
Zeros1966043
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:40.565707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.882065101
Coefficient of variation (CV)1.26364096
Kurtosis-1.764335661
Mean3.072126675
Median Absolute Deviation (MAD)0
Skewness0.4763672748
Sum9830790
Variance15.07042945
MonotonicityNot monotonic
2022-02-27T15:33:40.695165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
01966043
61.4%
81171843
36.6%
751284
 
1.6%
104680
 
0.1%
94003
 
0.1%
12543
 
< 0.1%
6522
 
< 0.1%
5345
 
< 0.1%
2341
 
< 0.1%
4165
 
< 0.1%
Other values (5)226
 
< 0.1%
ValueCountFrequency (%)
01966043
61.4%
14
 
< 0.1%
2341
 
< 0.1%
3118
 
< 0.1%
4165
 
< 0.1%
5345
 
< 0.1%
6522
 
< 0.1%
751284
 
1.6%
81171843
36.6%
94003
 
0.1%
ValueCountFrequency (%)
142
 
< 0.1%
134
 
< 0.1%
12543
 
< 0.1%
1198
 
< 0.1%
104680
 
0.1%
94003
 
0.1%
81171843
36.6%
751284
 
1.6%
6522
 
< 0.1%
5345
 
< 0.1%

TOTAL_FLOWS_EXP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct3199995
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51954198.13
Minimum2293398
Maximum85071692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 MiB
2022-02-27T15:33:40.870224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2293398
5-th percentile2453567.7
Q13093567.5
median79894575
Q383507185
95-th percentile84759701.2
Maximum85071692
Range82778294
Interquartile range (IQR)80413617.5

Descriptive statistics

Standard deviation38057691.3
Coefficient of variation (CV)0.7325238897
Kurtosis-1.730002329
Mean51954198.13
Median Absolute Deviation (MAD)4368868
Skewness-0.5073166442
Sum1.662531742 × 1014
Variance1.448387867 × 1015
MonotonicityNot monotonic
2022-02-27T15:33:41.046326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
838860801
 
< 0.1%
799122541
 
< 0.1%
799245161
 
< 0.1%
841167731
 
< 0.1%
841229181
 
< 0.1%
841041751
 
< 0.1%
799490961
 
< 0.1%
799470491
 
< 0.1%
799531941
 
< 0.1%
799511471
 
< 0.1%
Other values (3199985)3199985
> 99.9%
ValueCountFrequency (%)
22933981
< 0.1%
22934001
< 0.1%
22934041
< 0.1%
22934071
< 0.1%
22934091
< 0.1%
22934101
< 0.1%
22934121
< 0.1%
22934131
< 0.1%
22934161
< 0.1%
22934171
< 0.1%
ValueCountFrequency (%)
850716921
< 0.1%
850716851
< 0.1%
850716821
< 0.1%
850716811
< 0.1%
850716801
< 0.1%
850716791
< 0.1%
850716781
< 0.1%
850716771
< 0.1%
850716751
< 0.1%
850716741
< 0.1%

LABEL
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.4 MiB
SYN Scan - aggressive
800000 
Denial of Service R-U-Dead-Yet
800000 
Denial of Service Slowloris
800000 
Normal flow
799995 

Length

Max length30
Median length27
Mean length22.25001758
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal flow
2nd rowNormal flow
3rd rowNormal flow
4th rowNormal flow
5th rowNormal flow

Common Values

ValueCountFrequency (%)
SYN Scan - aggressive800000
25.0%
Denial of Service R-U-Dead-Yet800000
25.0%
Denial of Service Slowloris800000
25.0%
Normal flow799995
25.0%

Length

2022-02-27T15:33:41.216108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-27T15:33:41.386732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
denial1600000
14.3%
of1600000
14.3%
service1600000
14.3%
syn800000
7.1%
scan800000
7.1%
800000
7.1%
aggressive800000
7.1%
r-u-dead-yet800000
7.1%
slowloris800000
7.1%
normal799995
7.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-27T15:33:05.723829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:26:57.157141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:14.353659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:32.197460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:50.011811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:07.706570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:25.533237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:42.942331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:01.225342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:19.550522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:37.798803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:55.412230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:13.366363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:31.374625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:49.422035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:07.431231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:24.661549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:42.178132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:59.478865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:16.601585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:32.904791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:49.152478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:33:06.506015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:26:57.960064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:15.134897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:32.985116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:50.805876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:08.512047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:26.298098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:43.776599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:02.052147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:20.355021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:38.598357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:56.224674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:14.175342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:32.175632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:50.229526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:08.239245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:25.446715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:42.953349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:00.282381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:17.325292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:33.618058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:49.894213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:33:07.308542image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-02-27T15:28:23.939123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:41.336753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:59.459557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:17.922660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:36.139200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:53.816570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:11.683968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:29.746749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:47.791692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:05.751314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:23.076832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:40.626690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:57.900130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:15.117085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:31.455481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:47.675890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:33:04.090500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:33:22.271727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:13.585881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:31.411303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:27:49.212233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:06.929342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:24.770417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:28:42.141757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:00.278985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:18.761881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:36.992785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:29:54.610175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:12.530113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:30.566154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:30:48.620500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:06.577647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:23.876384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:41.420759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:31:58.686281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:15.882383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:32.194048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:32:48.421875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-27T15:33:04.842935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-02-27T15:33:41.567575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-27T15:33:41.931319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-27T15:33:42.294887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-27T15:33:42.589014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-27T15:33:42.802294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-27T15:33:22.813318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-27T15:33:25.434362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DST_TO_SRC_SECOND_BYTESFIRST_SWITCHEDFLOW_DURATION_MICROSECONDSFLOW_DURATION_MILLISECONDSIN_BYTESIN_PKTSL4_DST_PORTL4_SRC_PORTLAST_SWITCHEDOUT_BYTESOUT_PKTSPROTOCOLPROTOCOL_MAPSRC_TO_DST_SECOND_BYTESTCP_FLAGSTCP_WIN_MAX_INTCP_WIN_MAX_OUTTCP_WIN_MIN_INTCP_WIN_MIN_OUTTCP_WIN_MSS_INTCP_WIN_MSS_OUTTCP_WIN_SCALE_INTCP_WIN_SCALE_OUTTOTAL_FLOWS_EXPLABEL
0401616660040339044160541223511616660040401604422102401024014600002293398Normal flow
101616660040006810016166600400011680000000002293400Normal flow
2104161666004044725441892246504431616660040104260189251221634122163400002293404Normal flow
3016166600400052187286107516166600400060522819208192014400802293407Normal flow
440161666004011141189244339762161666004040160189285020502000002293409Normal flow
540161666004010301189244352132161666004040160189285020502000002293410Normal flow
650431616660040241380241369919443521121616660040504316603699266424065535642406553514601430782293412Normal flow
7100916166600401748091744861944350740161666004010096604861266424065535642406553514601440782293413Normal flow
801616660040005214455107116166600400060522819208192014520202293416Normal flow
912016166600404515445570744434156016166600401203605707285010501000002293417Normal flow

Last rows

DST_TO_SRC_SECOND_BYTESFIRST_SWITCHEDFLOW_DURATION_MICROSECONDSFLOW_DURATION_MILLISECONDSIN_BYTESIN_PKTSL4_DST_PORTL4_SRC_PORTLAST_SWITCHEDOUT_BYTESOUT_PKTSPROTOCOLPROTOCOL_MAPSRC_TO_DST_SECOND_BYTESTCP_FLAGSTCP_WIN_MAX_INTCP_WIN_MAX_OUTTCP_WIN_MIN_INTCP_WIN_MIN_OUTTCP_WIN_MSS_INTCP_WIN_MSS_OUTTCP_WIN_SCALE_INTCP_WIN_SCALE_OUTTOTAL_FLOWS_EXPLABEL
319998560161824679160256212602561015118040127161824685152810601122764860279606486027960141014107885071674Denial of Service Slowloris
319998660161824679160291593602911006118040124161824685152810601122764860279606486027960141014107885071675Denial of Service Slowloris
319998760161824679160287513602871079118040128161824685252810601122764860279606486027960141014107885071677Denial of Service Slowloris
319998860161824679160203224602031069118040132161824685252810601122764860279606486027960141014107885071678Denial of Service Slowloris
319998960161824679160219181602191135118040133161824685252810601122764860279606486027960141014107885071679Denial of Service Slowloris
319999060161824679160285963602851015118040130161824685252810601122764860279606486027960141014107885071680Denial of Service Slowloris
319999160161824679160226727602261136118040134161824685252810601122764860279606486027960141014107885071681Denial of Service Slowloris
31999926016182467916014827660148925118040140161824685252810601122764860279606486027960141014107885071682Denial of Service Slowloris
31999936016182467916024952360249954118040135161824685252810601122764860279606486027960141014107885071685Denial of Service Slowloris
31999946016182467916023446760234997118040139161824685252810601122764860279606486027960141014107885071692Denial of Service Slowloris